## Ignorer les outliers relatifs à l'utilisation de l'échelle de confiance :  FALSE
## Résultats basés sur la l'échelle de confiance :  FALSE
## Version courte :  FALSE
## Avec analyse des profils de joueurs :  TRUE
## Nombre de participants à l'expérimentation :  138
## Nombre de participants se déclarant comme joueurs :  71
## Nombre de femmes se déclarant comme joueuses :  5
## Age médian des joueurs :  15

Removing Outliers

## [1] "Outliers BET SAVED SHEEPS: "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of participants :  138"
## [1] "Total number of outliers:  0"
## [1] "- total number of outliers motor task:  0"
## [1] "- total number of outliers perceptive task:  0"
## [1] "- total number of outliers logical task:  0"
## [1] "Total number of participants after removing outliers:  138"
## [1] "- motor:  137"
## [1] "- perceptive:  135"
## [1] "- logical:  135"

Modeling difficulty

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   5201.1   5226.3  -2596.6   5193.1     4048 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.3526 -0.8300 -0.4242  0.8932  2.8790 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.6054   0.7781  
## Number of obs: 4052, groups:  IDjoueur, 137
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.0751     0.1152  -9.332  < 2e-16 ***
## difficulty    3.0681     0.1759  17.439  < 2e-16 ***
## timeNorm     -0.4561     0.1217  -3.748 0.000178 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.529       
## timeNorm   -0.443 -0.179
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0      4052         0 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.9902403  
##  1st Qu.:-0.4701721  
##  Median : 0.0033326  
##  Mean   : 0.0007788  
##  3rd Qu.: 0.3936276  
##  Max.   : 1.8862696  
## [1] "Intercept: -1.08 1e-20 ***"
## [1] "Difficulty: 3.07 4.1e-68 ***"
## [1] "Time: -0.456 0.00018 ***"
## [1] "R2 fixed: 0.15"
## [1] "R2 mixed: 0.28"
## [1] "Cross Val: 0.64"
## [1] "AIC: 5200"
##           0%          25%          50%          75%         100% 
## -1.886269575 -0.393627612 -0.003332599  0.470172110  1.990240334

##           0%          25%          50%          75%         100% 
## -1.886269575 -0.393627612 -0.003332599  0.470172110  1.990240334

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'gam'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   4130.5   4155.6  -2061.2   4122.5     3988 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.3516 -0.6336  0.0687  0.6637  6.7192 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.306    1.143   
## Number of obs: 3992, groups:  IDjoueur, 135
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -2.5991     0.1595 -16.298   <2e-16 ***
## difficulty    8.5059     0.3294  25.820   <2e-16 ***
## timeNorm     -0.2737     0.1429  -1.915   0.0555 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.577       
## timeNorm   -0.360 -0.175
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0         0      3992 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-2.657865  
##  1st Qu.:-0.735389  
##  Median : 0.028691  
##  Mean   : 0.001613  
##  3rd Qu.: 0.626099  
##  Max.   : 3.372937  
## [1] "Intercept: -2.6 1e-59 ***"
## [1] "Difficulty: 8.51 5.2e-147 ***"
## [1] "Time: -0.274 0.056 ."
## [1] "R2 fixed: 0.51"
## [1] "R2 mixed: 0.65"
## [1] "Cross Val: 0.75"
## [1] "AIC: 4100"
##          0%         25%         50%         75%        100% 
## -3.37293734 -0.62609925 -0.02869066  0.73538942  2.65786548

##          0%         25%         50%         75%        100% 
## -3.37293734 -0.62609925 -0.02869066  0.73538942  2.65786548

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'gam'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   4534.1   4559.3  -2263.0   4526.1     3988 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.7316 -0.6985 -0.2545  0.7441  6.5713 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.716    1.31    
## Number of obs: 3992, groups:  IDjoueur, 135
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.7838     0.1550  -11.51   <2e-16 ***
## difficulty    5.5087     0.2316   23.79   <2e-16 ***
## timeNorm     -1.5119     0.1494  -10.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.439       
## timeNorm   -0.234 -0.428
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##      3992         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-2.533846  
##  1st Qu.:-0.912974  
##  Median :-0.198655  
##  Mean   : 0.004174  
##  3rd Qu.: 1.039877  
##  Max.   : 2.880385  
## [1] "Intercept: -1.78 1.2e-30 ***"
## [1] "Difficulty: 5.51 4.5e-125 ***"
## [1] "Time: -1.51 4.6e-24 ***"
## [1] "R2 fixed: 0.33"
## [1] "R2 mixed: 0.56"
## [1] "Cross Val: 0.71"
## [1] "AIC: 4500"
##         0%        25%        50%        75%       100% 
## -2.8803846 -1.0398766  0.1986546  0.9129742  2.5338456

##         0%        25%        50%        75%       100% 
## -2.8803846 -1.0398766  0.1986546  0.9129742  2.5338456

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'gam'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.3868, p-value = 0.017
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1575783 
## 
## [1] "pvg.on.level.m 0.16 0.017 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.97076, p-value = 0.3317
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.06451767

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.13247, p-value = 0.8946
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## 0.008804425

Playing board games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.5106, p-value = 0.1309
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.09902835

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.62045, p-value = 0.535
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04095474

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.7738, p-value = 0.439
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.05112954

Self efficacy and level for each task

## Warning: Removed 66 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.88051, p-value = 0.3786
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07279689
## Warning: Removed 65 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.8394, p-value = 0.06585
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1532654 
## 
## [1] "self.eff.on.level.s 0.15 0.066 ."
## Warning: Removed 65 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2297, p-value = 0.2188
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1024592

Risk aversion and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.1307, p-value = 0.2582
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07002504

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 4.1259, p-value = 3.693e-05
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##      tau 
## 0.257712 
## 
## [1] "risk.av.on.level.s 0.26 3.7e-05 ***"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 3.5474, p-value = 0.000389
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2215004 
## 
## [1] "risk.av.on.level.l 0.22 0.00039 ***"

Age and level for each task

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.5158, p-value = 0.1296
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.09111308
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.0341, p-value = 0.3011
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.06256922
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.0689, p-value = 0.2851
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.06467829

Sex and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -4.9541, p-value = 7.267e-07
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.3481195 
## 
## [1] "sexe.on.level.m -0.35 7.3e-07 ***"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.062643, p-value = 0.9501
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## 0.004434833

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.0239, p-value = 0.04299
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1432792 
## 
## [1] "sexe.on.level.l -0.14 0.043 *"

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 914, p-value = 7.355e-07
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.8206230 -0.3827381
## sample estimates:
## difference in location 
##             -0.5955125 
## 
## [1] "sexe.on.level.m.2 -0.6 7.4e-07 *** mean(A): 0.18 mean(B): -0.43"

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 1913, p-value = 0.952
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.3967430  0.4026112
## sample estimates:
## difference in location 
##             0.01648856

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 1480, p-value = 0.04323
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.99451258 -0.01216139
## sample estimates:
## difference in location 
##              -0.541672 
## 
## [1] "sexe.on.level.l.2 -0.54 0.043 * mean(A): 0.12 mean(B): -0.3"

Influence of Objective difficulty on Subjective Difficulty

All tasks

##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda        pval
##  1:      0.03125            4.342716e-05   316    87     0.057 .
##  2:      0.09375            3.384905e-05   312   201     0.17 :(
##  3:      0.15625            7.829454e-05   303   311     0.69 :(
##  4:      0.21875            2.149488e-02   319   444     0.036 *
##  5:      0.28125           -2.904618e-05   286   625     0.92 :(
##  6:      0.34375            9.885961e-03   260   840      0.5 :(
##  7:      0.40625            3.925970e-02   274   899   0.0097 **
##  8:      0.46875            8.233885e-02   267   941 7.6e-07 ***
##  9:      0.53125            1.038859e-01   234   847   7e-11 ***
## 10:      0.59375            1.098386e-01   284   635 1.5e-11 ***
## 11:      0.65625            1.214842e-01   258   511 2.7e-12 ***
## 12:      0.71875            1.142805e-01   305   313 2.7e-10 ***
## 13:      0.78125            1.099682e-01   332   166   1e-06 ***
## 14:      0.84375            9.531926e-02   305   108    0.002 **
## 15:      0.90625            1.211802e-05   339    48     0.52 :(
## 16:      0.96875            3.477634e-05   652    14     0.88 :(

##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda        pval
##  1:      0.03125            2.419693e-05   291    70      0.8 :(
##  2:      0.09375           -2.141665e-05   226    88     0.22 :(
##  3:      0.15625           -1.612657e-05   196    91     0.15 :(
##  4:      0.21875           -2.043126e-02   203   131     0.32 :(
##  5:      0.28125           -7.135355e-02   177   166   0.0024 **
##  6:      0.34375            1.423581e-02   123   195     0.37 :(
##  7:      0.40625            7.931007e-02   138   191   0.0023 **
##  8:      0.46875            1.094841e-01   129   174 0.00035 ***
##  9:      0.53125            1.357438e-01   123   197 4.4e-07 ***
## 10:      0.59375            7.619211e-02   149   145    0.003 **
## 11:      0.65625            1.020189e-01   116   106   0.0045 **
## 12:      0.71875            1.429148e-01   126    64 0.00013 ***
## 13:      0.78125            2.377444e-02   127    32     0.57 :(
## 14:      0.84375            1.428174e-01    91    18     0.017 *
## 15:      0.90625           -7.142305e-02   107    11     0.38 :(
## 16:      0.96875            0.000000e+00   230     1        <NA>
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties

##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda        pval
##  1:      0.03125            1.427794e-01    25    17 0.00057 ***
##  2:      0.09375            3.545860e-05    84   111 0.00025 ***
##  3:      0.15625            5.239006e-02   106   149   0.0095 **
##  4:      0.21875            4.382500e-05   105   220     0.44 :(
##  5:      0.28125           -1.187233e-02    99   281     0.39 :(
##  6:      0.34375           -7.620086e-02   118   421   0.0054 **
##  7:      0.40625           -2.699148e-05   122   488      0.9 :(
##  8:      0.46875            1.111250e-01   111   510 0.00016 ***
##  9:      0.53125            1.189819e-01    86   394 7.9e-05 ***
## 10:      0.59375            1.809004e-01    99   285 2.2e-10 ***
## 11:      0.65625            1.904793e-01   101   206 1.6e-08 ***
## 12:      0.71875            1.071804e-01   142   139   0.0017 **
## 13:      0.78125            9.517804e-02   153    86   0.0047 **
## 14:      0.84375            1.061886e-05   145    55     0.37 :(
## 15:      0.90625           -2.038385e-02   130    23     0.29 :(
## 16:      0.96875            0.000000e+00   259     5        <NA>
## Warning: Removed 1 rows containing missing values (geom_point).

##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda        pval
##  1:      0.03125            0.000000e+00     0     0        <NA>
##  2:      0.09375            0.000000e+00     2     2        <NA>
##  3:      0.15625            0.000000e+00     1    71        <NA>
##  4:      0.21875            7.138258e-02    11    93      0.3 :(
##  5:      0.28125            0.000000e+00    10   178        <NA>
##  6:      0.34375            2.999858e-01    19   224 0.00065 ***
##  7:      0.40625            1.904980e-01    14   220     0.099 .
##  8:      0.46875           -2.377865e-02    27   257     0.76 :(
##  9:      0.53125           -4.762544e-05    25   256     0.97 :(
## 10:      0.59375           -8.576225e-02    36   205     0.084 .
## 11:      0.65625            7.138877e-02    41   199     0.14 :(
## 12:      0.71875            1.530535e-01    37   110 0.00077 ***
## 13:      0.78125            1.428764e-01    52    48   0.0033 **
## 14:      0.84375            7.147380e-02    69    35     0.11 :(
## 15:      0.90625            4.286188e-01   102    14 0.00057 ***
## 16:      0.96875            0.000000e+00   163     8        <NA>
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).

Motor task

##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda        pval
##  1:      0.03125            0.000000e+00     0     0        <NA>
##  2:      0.09375            0.000000e+00    18     3        <NA>
##  3:      0.15625            2.618201e-05    92    22     0.44 :(
##  4:      0.21875           -4.082045e-05   145    75     0.25 :(
##  5:      0.28125           -7.117675e-03   135   175     0.19 :(
##  6:      0.34375            2.375475e-02   127   329     0.15 :(
##  7:      0.40625            3.568020e-02   138   444      0.1 :(
##  8:      0.46875            9.520208e-02   126   441   0.0016 **
##  9:      0.53125            1.548070e-01   126   374 8.9e-10 ***
## 10:      0.59375            1.784991e-01   136   263 7.9e-09 ***
## 11:      0.65625            2.499345e-01   133   159 2.9e-10 ***
## 12:      0.71875            2.856551e-01   162    63 9.2e-07 ***
## 13:      0.78125            3.928219e-01   152    18 1.1e-06 ***
## 14:      0.84375            0.000000e+00    92     4        <NA>
## 15:      0.90625            0.000000e+00    70     0        <NA>
## 16:      0.96875            0.000000e+00    30     0        <NA>
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_point).

## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda        pval
##  1:      0.03125            0.000000e+00     0     0        <NA>
##  2:      0.09375            0.000000e+00    18     3        <NA>
##  3:      0.15625            3.396854e-05    86    21     0.47 :(
##  4:      0.21875           -6.670841e-06   100    50     0.35 :(
##  5:      0.28125           -1.190892e-01    82    80 0.00072 ***
##  6:      0.34375            6.118190e-02    49    99     0.36 :(
##  7:      0.40625            1.076873e-01    65   103     0.014 *
##  8:      0.46875            1.428563e-01    58    81   0.0072 **
##  9:      0.53125            1.786171e-01    63   106 1.1e-06 ***
## 10:      0.59375            1.428118e-01    65    60     0.015 *
## 11:      0.65625            4.000764e-01    49    44 9.5e-05 ***
## 12:      0.71875            6.932262e-06    58    12     0.62 :(
## 13:      0.78125            0.000000e+00    32     1        <NA>
## 14:      0.84375            0.000000e+00     0     0        <NA>
## 15:      0.90625            0.000000e+00     0     0        <NA>
## 16:      0.96875            0.000000e+00     0     0        <NA>
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 6 rows containing missing values (geom_point).
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties

##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda        pval
##  1:      0.03125            0.000000e+00     0     0        <NA>
##  2:      0.09375            0.000000e+00     0     0        <NA>
##  3:      0.15625            0.000000e+00     6     1        <NA>
##  4:      0.21875           -5.098858e-06    45    25     0.36 :(
##  5:      0.28125            4.541290e-06    53    95     0.31 :(
##  6:      0.34375            2.143476e-05    76   210     0.53 :(
##  7:      0.40625           -2.131138e-05    70   299     0.64 :(
##  8:      0.46875            2.861704e-02    61   318     0.25 :(
##  9:      0.53125            1.428899e-01    54   223   6e-05 ***
## 10:      0.59375            2.143013e-01    53   125 2.4e-07 ***
## 11:      0.65625            3.332932e-01    64    71 3.3e-07 ***
## 12:      0.71875            3.333021e-01    84    32 1.9e-06 ***
## 13:      0.78125            0.000000e+00    88     9        <NA>
## 14:      0.84375            0.000000e+00    60     2        <NA>
## 15:      0.90625            0.000000e+00    11     0        <NA>
## 16:      0.96875            0.000000e+00     0     0        <NA>
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties

##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda    pval
##  1:      0.03125               0.0000000     0     0    <NA>
##  2:      0.09375               0.0000000     0     0    <NA>
##  3:      0.15625               0.0000000     0     0    <NA>
##  4:      0.21875               0.0000000     0     0    <NA>
##  5:      0.28125               0.0000000     0     0    <NA>
##  6:      0.34375               0.0000000     2    20    <NA>
##  7:      0.40625               0.0000000     3    42    <NA>
##  8:      0.46875               0.0000000     7    42    <NA>
##  9:      0.53125               0.0000000     9    45    <NA>
## 10:      0.59375              -0.0857674    18    78 0.12 :(
## 11:      0.65625              -0.0238951    20    44 0.53 :(
## 12:      0.71875               0.1053921    20    19 0.35 :(
## 13:      0.78125               0.0000000    32     8    <NA>
## 14:      0.84375               0.0000000    32     2    <NA>
## 15:      0.90625               0.0000000    59     0    <NA>
## 16:      0.96875               0.0000000    30     0    <NA>
## Warning: Removed 9 rows containing missing values (geom_point).
## Warning: Removed 9 rows containing missing values (geom_point).

Sensory task

##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda        pval
##  1:      0.03125            0.000000e+00   160    32 0.00073 ***
##  2:      0.09375            5.473331e-05   139    95 3.5e-05 ***
##  3:      0.15625            4.350596e-05    97   153     0.94 :(
##  4:      0.21875            6.609701e-05    67   175      0.3 :(
##  5:      0.28125           -3.565305e-02    70   225     0.065 .
##  6:      0.34375           -3.964448e-02    56   252     0.13 :(
##  7:      0.40625           -1.026174e-02    54   187     0.52 :(
##  8:      0.46875            2.379528e-02    59   235      0.6 :(
##  9:      0.53125           -7.382765e-05    43   227     0.76 :(
## 10:      0.59375           -7.137519e-02    53   166     0.097 .
## 11:      0.65625           -4.404848e-05    54   195     0.72 :(
## 12:      0.71875           -1.905050e-02    59   137     0.43 :(
## 13:      0.78125           -3.568004e-02    79    93      0.4 :(
## 14:      0.84375           -1.287757e-05    87    81     0.94 :(
## 15:      0.90625            4.676427e-05   142    43     0.69 :(
## 16:      0.96875            1.784999e-02   463    14     0.31 :(

## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda        pval
##  1:      0.03125            0.000000e+00   146    19     0.013 *
##  2:      0.09375            1.910045e-05    91    20      0.2 :(
##  3:      0.15625            4.514092e-05    51    16     0.91 :(
##  4:      0.21875           -9.520158e-02    37    21     0.15 :(
##  5:      0.28125           -4.985487e-05    49    19     0.56 :(
##  6:      0.34375            9.384204e-05    32    23     0.53 :(
##  7:      0.40625           -1.714910e-01    31    21     0.034 *
##  8:      0.46875            2.385828e-02    34    18     0.15 :(
##  9:      0.53125            7.756459e-05    25    22     0.83 :(
## 10:      0.59375           -3.265245e-01    35    22 0.00025 ***
## 11:      0.65625           -1.633143e-01    37    17     0.018 *
## 12:      0.71875            1.904531e-01    31    14     0.037 *
## 13:      0.78125           -1.428628e-01    49    14     0.034 *
## 14:      0.84375            4.808356e-05    51    13     0.67 :(
## 15:      0.90625            0.000000e+00    86    10        <NA>
## 16:      0.96875            0.000000e+00   230     1        <NA>
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties

##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda        pval
##  1:      0.03125            1.198414e-01    14    13   0.0044 **
##  2:      0.09375            4.279747e-07    48    75 0.00014 ***
##  3:      0.15625            6.254372e-05    46    83      0.1 :(
##  4:      0.21875            6.122825e-05    28   105     0.87 :(
##  5:      0.28125           -3.565279e-02    20   105     0.16 :(
##  6:      0.34375           -1.818199e-01    21   128   9e-04 ***
##  7:      0.40625            1.428710e-01    23   110     0.069 .
##  8:      0.46875            2.856472e-01    24   123 0.00061 ***
##  9:      0.53125            6.006544e-05    15   109     0.96 :(
## 10:      0.59375            1.428976e-01    18   100     0.057 .
## 11:      0.65625            7.137593e-02    16    89     0.55 :(
## 12:      0.71875           -3.912901e-05    28    71     0.86 :(
## 13:      0.78125            1.827680e-05    30    54     0.65 :(
## 14:      0.84375            6.070557e-06    35    41     0.89 :(
## 15:      0.90625           -7.362921e-05    56    19     0.17 :(
## 16:      0.96875            0.000000e+00   216     5        <NA>
## Warning: Removed 1 rows containing missing values (geom_point).

##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
##  1:      0.03125                       0     0     0   NA
##  2:      0.09375                       0     0     0   NA
##  3:      0.15625                       0     0    54   NA
##  4:      0.21875                       0     2    49   NA
##  5:      0.28125                       0     1   101   NA
##  6:      0.34375                       0     3   101   NA
##  7:      0.40625                       0     0    56   NA
##  8:      0.46875                       0     1    94   NA
##  9:      0.53125                       0     3    96   NA
## 10:      0.59375                       0     0    44   NA
## 11:      0.65625                       0     1    89   NA
## 12:      0.71875                       0     0    52   NA
## 13:      0.78125                       0     0    25   NA
## 14:      0.84375                       0     1    27   NA
## 15:      0.90625                       0     0    14   NA
## 16:      0.96875                       0    17     8   NA
## Warning: Removed 15 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).

Logical task

##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda        pval
##  1:      0.03125            4.512536e-05   156    55     0.84 :(
##  2:      0.09375           -4.846520e-05   155   103     0.87 :(
##  3:      0.15625            2.385238e-02   114   136     0.081 .
##  4:      0.21875            7.150552e-02   107   194     0.017 *
##  5:      0.28125            4.764744e-02    81   225     0.14 :(
##  6:      0.34375            3.332034e-05    77   259     0.75 :(
##  7:      0.40625            7.138641e-02    82   268     0.044 *
##  8:      0.46875            1.071922e-01    82   265   0.0015 **
##  9:      0.53125            1.190605e-01    65   246   0.0046 **
## 10:      0.59375            1.072026e-01    95   206 0.00072 ***
## 11:      0.65625            1.428217e-01    71   157   0.0037 **
## 12:      0.71875            1.429032e-01    84   113 4.8e-05 ***
## 13:      0.78125            7.149725e-02   101    55     0.055 .
## 14:      0.84375            2.142376e-01   126    23 0.00079 ***
## 15:      0.90625            0.000000e+00   127     5        <NA>
## 16:      0.96875            0.000000e+00   159     0        <NA>
## Warning: Removed 2 rows containing missing values (geom_point).

## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda        pval
##  1:      0.03125           -5.584448e-06   145    51     0.73 :(
##  2:      0.09375           -2.027060e-05   117    65      0.6 :(
##  3:      0.15625           -3.045394e-05    59    54     0.87 :(
##  4:      0.21875            4.170671e-05    66    60     0.72 :(
##  5:      0.28125            2.859194e-02    46    67      0.5 :(
##  6:      0.34375            4.286626e-02    42    73     0.51 :(
##  7:      0.40625            2.142231e-01    42    67 0.00036 ***
##  8:      0.46875            1.428347e-01    37    75     0.048 *
##  9:      0.53125            1.427869e-01    35    69     0.011 *
## 10:      0.59375            1.667295e-01    49    63 0.00035 ***
## 11:      0.65625            1.277801e-06    30    45     0.85 :(
## 12:      0.71875            1.071069e-01    37    38     0.053 .
## 13:      0.78125            1.429283e-01    46    17     0.051 .
## 14:      0.84375            0.000000e+00    40     5        <NA>
## 15:      0.90625            0.000000e+00    21     1        <NA>
## 16:      0.96875            0.000000e+00     0     0        <NA>
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties

##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda      pval
##  1:      0.03125            0.000000e+00    11     4      <NA>
##  2:      0.09375            5.723969e-05    36    36   0.17 :(
##  3:      0.15625            1.071864e-01    54    65   0.11 :(
##  4:      0.21875            2.496434e-01    32    90 2e-04 ***
##  5:      0.28125           -7.383892e-06    26    81   0.73 :(
##  6:      0.34375           -1.666531e-01    21    83   0.094 .
##  7:      0.40625           -1.428407e-01    29    79   0.035 *
##  8:      0.46875            1.428168e-01    26    69   0.066 .
##  9:      0.53125            1.456272e-02    17    62   0.47 :(
## 10:      0.59375            7.139054e-02    28    60   0.19 :(
## 11:      0.65625            1.071899e-01    21    46   0.15 :(
## 12:      0.71875            9.481998e-05    30    36    0.4 :(
## 13:      0.78125           -2.380948e-02    35    23   0.39 :(
## 14:      0.84375            1.143389e-01    50    12   0.18 :(
## 15:      0.90625            0.000000e+00    63     4      <NA>
## 16:      0.96875            0.000000e+00    43     0      <NA>
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties

##     obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda        pval
##  1:      0.03125              0.00000000     0     0        <NA>
##  2:      0.09375              0.00000000     2     2        <NA>
##  3:      0.15625              0.00000000     1    17        <NA>
##  4:      0.21875              0.00000000     9    44        <NA>
##  5:      0.28125              0.00000000     9    77        <NA>
##  6:      0.34375              0.29767829    14   103 6.7e-05 ***
##  7:      0.40625              0.23805085    11   122     0.018 *
##  8:      0.46875              0.08337811    19   121     0.11 :(
##  9:      0.53125              0.07143520    13   115     0.23 :(
## 10:      0.59375              0.02853096    18    83     0.57 :(
## 11:      0.65625              0.28567004    20    66   0.0019 **
## 12:      0.71875              0.35710077    17    39 3.8e-06 ***
## 13:      0.78125              0.14288629    20    15      0.03 *
## 14:      0.84375              0.00000000    36     6        <NA>
## 15:      0.90625              0.00000000    43     0        <NA>
## 16:      0.96875              0.00000000   116     0        <NA>
## Warning: Removed 5 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_point).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## Warning: Removed 2370 rows containing non-finite values (stat_bin2d).

## Warning: Removed 2310 rows containing non-finite values (stat_bin2d).

## Warning: Removed 2310 rows containing non-finite values (stat_bin2d).

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7101  -0.1860  -0.0144   0.1830   0.6941  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.24752    0.01826  13.556   <2e-16 ***
## timeNorm     0.03373    0.02285   1.476     0.14    
## obj.diff    -0.56227    0.01983 -28.351   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06545186)
## 
##     Null deviance: 166.13  on 1681  degrees of freedom
## Residual deviance: 109.89  on 1679  degrees of freedom
##   (2310 observations deleted due to missingness)
## AIC: 192.43
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff   n    pval
##  1:      1.5             NA     0.4840110 -0.050845983 353 0.053 .
##  2:      4.5             NA     0.5009994 -0.024626313 411 0.21 :(
##  3:      7.5             NA     0.4940760 -0.019878578 411 0.32 :(
##  4:     10.5             NA     0.4938841  0.011167138 411  0.6 :(
##  5:     13.5             NA     0.4840366 -0.001073084 411 0.95 :(
##  6:     16.5             NA     0.4943476 -0.002958823 411 0.89 :(
##  7:     19.5             NA     0.4964643 -0.033397611 411  0.07 .
##  8:     22.5             NA     0.4846435 -0.010529685 411  0.6 :(
##  9:     25.5             NA     0.4802931 -0.001036981 411 0.96 :(
## 10:     28.5             NA     0.4629047  0.009977788 411 0.63 :(
##     time   error.diff shapes
##  1:  1.5 -0.050845983     16
##  2:  4.5 -0.024626313     16
##  3:  7.5 -0.019878578     16
##  4: 10.5  0.011167138     16
##  5: 13.5 -0.001073084     16
##  6: 16.5 -0.002958823     16
##  7: 19.5 -0.033397611     16
##  8: 22.5 -0.010529685     16
##  9: 25.5 -0.001036981     16
## 10: 28.5  0.009977788     16

##     time.bin subj.diff.mean obj.diff.mean error.diff  n        pval
##  1:      1.5             NA     0.2432479 0.12917006 80     0.053 .
##  2:      4.5             NA     0.2562790 0.14093419 76   0.0031 **
##  3:      7.5             NA     0.2598344 0.07993451 80   0.0097 **
##  4:     10.5             NA     0.2634087 0.07199009 80     0.032 *
##  5:     13.5             NA     0.2534150 0.06403160 73     0.027 *
##  6:     16.5             NA     0.2556853 0.06466362 71     0.089 .
##  7:     19.5             NA     0.2547723 0.02113616 96     0.42 :(
##  8:     22.5             NA     0.2431613 0.07633368 83     0.026 *
##  9:     25.5             NA     0.2438608 0.12507700 79 0.00085 ***
## 10:     28.5             NA     0.2354276 0.08287540 83   0.0022 **
##     time error.diff shapes
##  1:  1.5 0.12917006     16
##  2:  4.5 0.14093419     24
##  3:  7.5 0.07993451     24
##  4: 10.5 0.07199009     24
##  5: 13.5 0.06403160     24
##  6: 16.5 0.06466362     16
##  7: 19.5 0.02113616     16
##  8: 22.5 0.07633368     24
##  9: 25.5 0.12507700     24
## 10: 28.5 0.08287540     24
## Warning: Removed 3 rows containing missing values (geom_errorbar).

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n    pval
##  1:      1.5             NA     0.4795679  0.03054758 207  0.4 :(
##  2:      4.5             NA     0.4897145 -0.03719132 258 0.15 :(
##  3:      7.5             NA     0.4849785 -0.02554995 254 0.31 :(
##  4:     10.5             NA     0.4913258  0.06037396 269 0.036 *
##  5:     13.5             NA     0.4754936  0.04222385 277 0.18 :(
##  6:     16.5             NA     0.4903172  0.04683914 274 0.12 :(
##  7:     19.5             NA     0.5014546 -0.02187366 228 0.45 :(
##  8:     22.5             NA     0.4839376  0.02305690 256 0.45 :(
##  9:     25.5             NA     0.4896862 -0.01617450 278 0.48 :(
## 10:     28.5             NA     0.4808808  0.04489820 285 0.11 :(
##     time  error.diff shapes
##  1:  1.5  0.03054758     16
##  2:  4.5 -0.03719132     16
##  3:  7.5 -0.02554995     16
##  4: 10.5  0.06037396     24
##  5: 13.5  0.04222385     16
##  6: 16.5  0.04683914     16
##  7: 19.5 -0.02187366     16
##  8: 22.5  0.02305690     16
##  9: 25.5 -0.01617450     16
## 10: 28.5  0.04489820     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5             NA     0.7897802 -0.17785100 66 9.9e-06 ***
##  2:      4.5             NA     0.7803532 -0.09749158 77   0.0044 **
##  3:      7.5             NA     0.7674539 -0.08928525 77   0.0077 **
##  4:     10.5             NA     0.8023715 -0.13009804 62   0.0022 **
##  5:     13.5             NA     0.7988200 -0.12530366 61 0.00085 ***
##  6:     16.5             NA     0.7678228 -0.11058229 66   0.0013 **
##  7:     19.5             NA     0.7500808 -0.07733665 87   0.0013 **
##  8:     22.5             NA     0.7655285 -0.12989773 72 0.00029 ***
##  9:     25.5             NA     0.7778278 -0.11969210 54    0.002 **
## 10:     28.5             NA     0.7828447 -0.16338955 43   6e-06 ***
##     time  error.diff shapes
##  1:  1.5 -0.17785100     24
##  2:  4.5 -0.09749158     24
##  3:  7.5 -0.08928525     24
##  4: 10.5 -0.13009804     24
##  5: 13.5 -0.12530366     24
##  6: 16.5 -0.11058229     24
##  7: 19.5 -0.07733665     24
##  8: 22.5 -0.12989773     24
##  9: 25.5 -0.11969210     24
## 10: 28.5 -0.16338955     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5             NA     0.3556457 -0.14643400 347 5.7e-06 ***
##  2:      4.5             NA     0.5101236 -0.10385762 405 1.4e-07 ***
##  3:      7.5             NA     0.5056170 -0.07527783 405 0.00057 ***
##  4:     10.5             NA     0.5365403 -0.08887349 405   3e-05 ***
##  5:     13.5             NA     0.5187050 -0.10083041 405   4e-07 ***
##  6:     16.5             NA     0.5109441 -0.12902174 405 1.6e-07 ***
##  7:     19.5             NA     0.5236235 -0.08423175 405 1.5e-05 ***
##  8:     22.5             NA     0.5423005 -0.07593718 405 0.00012 ***
##  9:     25.5             NA     0.5324158 -0.04607646 405     0.014 *
## 10:     28.5             NA     0.5318888 -0.08687999 405   1e-04 ***
##     time  error.diff shapes
##  1:  1.5 -0.14643400     24
##  2:  4.5 -0.10385762     24
##  3:  7.5 -0.07527783     24
##  4: 10.5 -0.08887349     24
##  5: 13.5 -0.10083041     24
##  6: 16.5 -0.12902174     24
##  7: 19.5 -0.08423175     24
##  8: 22.5 -0.07593718     24
##  9: 25.5 -0.04607646     24
## 10: 28.5 -0.08687999     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5             NA     0.1532464  0.08749165 210   0.11 :(
##  2:      4.5             NA     0.1777012  0.08989797 132   0.098 .
##  3:      7.5             NA     0.1874267  0.09152168 129   0.041 *
##  4:     10.5             NA     0.1880539  0.07469314 108   0.18 :(
##  5:     13.5             NA     0.1923434  0.04290497 115   0.51 :(
##  6:     16.5             NA     0.1903028 -0.01587250 127    0.8 :(
##  7:     19.5             NA     0.1665092  0.02699334 154   0.49 :(
##  8:     22.5             NA     0.1861060  0.11964834 105   0.013 *
##  9:     25.5             NA     0.1962599  0.13955600 119 0.0024 **
## 10:     28.5             NA     0.1847045  0.12594412 118  0.007 **
##     time  error.diff shapes
##  1:  1.5  0.08749165     16
##  2:  4.5  0.08989797     16
##  3:  7.5  0.09152168     24
##  4: 10.5  0.07469314     16
##  5: 13.5  0.04290497     16
##  6: 16.5 -0.01587250     16
##  7: 19.5  0.02699334     16
##  8: 22.5  0.11964834     24
##  9: 25.5  0.13955600     24
## 10: 28.5  0.12594412     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n    pval
##  1:      1.5             NA     0.4419674 -0.17368146  67 0.048 *
##  2:      4.5             NA     0.4890926 -0.03303027 146 0.52 :(
##  3:      7.5             NA     0.4978024 -0.08199182 151 0.13 :(
##  4:     10.5             NA     0.5046928 -0.05553930 168 0.42 :(
##  5:     13.5             NA     0.4780767 -0.13584537 159 0.041 *
##  6:     16.5             NA     0.4984364 -0.15521906 151 0.052 .
##  7:     19.5             NA     0.5045227 -0.06069415  89 0.29 :(
##  8:     22.5             NA     0.5028234 -0.10506038 162 0.048 *
##  9:     25.5             NA     0.4959545 -0.09734223 150 0.17 :(
## 10:     28.5             NA     0.5050058 -0.17334577 153 0.014 *
##     time  error.diff shapes
##  1:  1.5 -0.17368146     24
##  2:  4.5 -0.03303027     16
##  3:  7.5 -0.08199182     16
##  4: 10.5 -0.05553930     16
##  5: 13.5 -0.13584537     24
##  6: 16.5 -0.15521906     16
##  7: 19.5 -0.06069415     16
##  8: 22.5 -0.10506038     24
##  9: 25.5 -0.09734223     16
## 10: 28.5 -0.17334577     24

##     time.bin subj.diff.mean obj.diff.mean error.diff   n        pval
##  1:      1.5             NA     0.8802217 -0.2459247  70   9e-09 ***
##  2:      4.5             NA     0.8798108 -0.2387529 127 1.3e-12 ***
##  3:      7.5             NA     0.8434293 -0.2125622 125 1.7e-07 ***
##  4:     10.5             NA     0.8697723 -0.1983531 129 9.8e-09 ***
##  5:     13.5             NA     0.8545178 -0.2015393 131 3.7e-08 ***
##  6:     16.5             NA     0.8464567 -0.2285818 127   6e-09 ***
##  7:     19.5             NA     0.8735961 -0.2107135 162   4e-08 ***
##  8:     22.5             NA     0.8596608 -0.1991268 138 1.5e-07 ***
##  9:     25.5             NA     0.8667667 -0.1664666 136 8.8e-06 ***
## 10:     28.5             NA     0.8683130 -0.2015478 134 5.7e-09 ***
##     time error.diff shapes
##  1:  1.5 -0.2459247     24
##  2:  4.5 -0.2387529     24
##  3:  7.5 -0.2125622     24
##  4: 10.5 -0.1983531     24
##  5: 13.5 -0.2015393     24
##  6: 16.5 -0.2285818     24
##  7: 19.5 -0.2107135     24
##  8: 22.5 -0.1991268     24
##  9: 25.5 -0.1664666     24
## 10: 28.5 -0.2015478     24

##     time.bin subj.diff.mean obj.diff.mean    error.diff   n        pval
##  1:      1.5             NA     0.3735435 -0.1321485500 347 7.8e-05 ***
##  2:      4.5             NA     0.4622814 -0.1222423528 405 7.1e-06 ***
##  3:      7.5             NA     0.4719065 -0.0401721647 405     0.11 :(
##  4:     10.5             NA     0.4758802 -0.0005060253 405     0.98 :(
##  5:     13.5             NA     0.4802303 -0.0075009274 405     0.76 :(
##  6:     16.5             NA     0.4878267 -0.0048228149 405     0.84 :(
##  7:     19.5             NA     0.4701706 -0.0063255874 405     0.77 :(
##  8:     22.5             NA     0.4277367 -0.0139269309 405     0.57 :(
##  9:     25.5             NA     0.4256454  0.0502729816 405     0.055 .
## 10:     28.5             NA     0.4219079  0.0452511671 405     0.065 .
##     time    error.diff shapes
##  1:  1.5 -0.1321485500     24
##  2:  4.5 -0.1222423528     24
##  3:  7.5 -0.0401721647     16
##  4: 10.5 -0.0005060253     16
##  5: 13.5 -0.0075009274     16
##  6: 16.5 -0.0048228149     16
##  7: 19.5 -0.0063255874     16
##  8: 22.5 -0.0139269309     16
##  9: 25.5  0.0502729816     16
## 10: 28.5  0.0452511671     16

##     time.bin subj.diff.mean obj.diff.mean error.diff   n        pval
##  1:      1.5             NA     0.1444702  0.1292499 186   0.0067 **
##  2:      4.5             NA     0.1797062  0.1736101 156   0.0036 **
##  3:      7.5             NA     0.1891977  0.1661685 135 1.9e-05 ***
##  4:     10.5             NA     0.2000008  0.1817844 126 1.6e-06 ***
##  5:     13.5             NA     0.2005678  0.2039673 109 7.4e-07 ***
##  6:     16.5             NA     0.1940743  0.1654151 102 6.2e-06 ***
##  7:     19.5             NA     0.1751540  0.1389413 163   0.0014 **
##  8:     22.5             NA     0.1856054  0.1361224 153 0.00077 ***
##  9:     25.5             NA     0.1833445  0.1854309 153 3.5e-07 ***
## 10:     28.5             NA     0.1860644  0.1850076 161 1.9e-06 ***
##     time error.diff shapes
##  1:  1.5  0.1292499     24
##  2:  4.5  0.1736101     24
##  3:  7.5  0.1661685     24
##  4: 10.5  0.1817844     24
##  5: 13.5  0.2039673     24
##  6: 16.5  0.1654151     24
##  7: 19.5  0.1389413     24
##  8: 22.5  0.1361224     24
##  9: 25.5  0.1854309     24
## 10: 28.5  0.1850076     24
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_errorbar).

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n    pval
##  1:      1.5             NA     0.4986645 -0.088095070  94 0.16 :(
##  2:      4.5             NA     0.4929868  0.036113675 152 0.31 :(
##  3:      7.5             NA     0.4861101  0.058520341 166 0.15 :(
##  4:     10.5             NA     0.4918719  0.050765987 188 0.21 :(
##  5:     13.5             NA     0.4893625  0.024825306 212 0.59 :(
##  6:     16.5             NA     0.4907633  0.039949668 218 0.26 :(
##  7:     19.5             NA     0.5014750  0.052290286 113 0.16 :(
##  8:     22.5             NA     0.4888822  0.011822929 186 0.79 :(
##  9:     25.5             NA     0.4868837  0.067077231 182 0.054 .
## 10:     28.5             NA     0.4888791 -0.007535674 172  0.8 :(
##     time   error.diff shapes
##  1:  1.5 -0.088095070     16
##  2:  4.5  0.036113675     16
##  3:  7.5  0.058520341     16
##  4: 10.5  0.050765987     16
##  5: 13.5  0.024825306     16
##  6: 16.5  0.039949668     16
##  7: 19.5  0.052290286     16
##  8: 22.5  0.011822929     16
##  9: 25.5  0.067077231     16
## 10: 28.5 -0.007535674     16

##     time.bin subj.diff.mean obj.diff.mean error.diff   n        pval
##  1:      1.5             NA     0.8339353 -0.3233520  67 5.6e-09 ***
##  2:      4.5             NA     0.8686165 -0.3387834  97 4.9e-14 ***
##  3:      7.5             NA     0.8162133 -0.2506208 104 8.9e-11 ***
##  4:     10.5             NA     0.8248292 -0.2086811  91 1.5e-07 ***
##  5:     13.5             NA     0.8200777 -0.2230776  84 4.2e-08 ***
##  6:     16.5             NA     0.8327977 -0.2036156  85 1.1e-07 ***
##  7:     19.5             NA     0.8155218 -0.2197435 129 2.3e-07 ***
##  8:     22.5             NA     0.8167217 -0.2483378  66 1.4e-06 ***
##  9:     25.5             NA     0.7960266 -0.2401919  70 8.9e-07 ***
## 10:     28.5             NA     0.7892935 -0.1649722  72 4.3e-05 ***
##     time error.diff shapes
##  1:  1.5 -0.3233520     24
##  2:  4.5 -0.3387834     24
##  3:  7.5 -0.2506208     24
##  4: 10.5 -0.2086811     24
##  5: 13.5 -0.2230776     24
##  6: 16.5 -0.2036156     24
##  7: 19.5 -0.2197435     24
##  8: 22.5 -0.2483378     24
##  9: 25.5 -0.2401919     24
## 10: 28.5 -0.1649722     24

For all taks, per group

## Warning: Removed 1920 rows containing non-finite values (stat_bin2d).

## Warning: Removed 3390 rows containing non-finite values (stat_bin2d).

## Warning: Removed 1680 rows containing non-finite values (stat_bin2d).

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5             NA     0.5370427 -0.27738939 234 6.7e-09 ***
##  2:      4.5             NA     0.5934534 -0.23994050 255 2.4e-07 ***
##  3:      7.5             NA     0.5884498 -0.17968026 255 0.00012 ***
##  4:     10.5             NA     0.5552892 -0.10234098 255     0.013 *
##  5:     13.5             NA     0.5545696 -0.20286547 255 0.00011 ***
##  6:     16.5             NA     0.5552245 -0.14224554 255   0.0029 **
##  7:     19.5             NA     0.5512077 -0.09181317 255   0.0049 **
##  8:     22.5             NA     0.5340527 -0.15808362 255 0.00026 ***
##  9:     25.5             NA     0.5067738 -0.09377907 255     0.035 *
## 10:     28.5             NA     0.5215155 -0.07756929 255     0.045 *
##     time  error.diff shapes
##  1:  1.5 -0.27738939     24
##  2:  4.5 -0.23994050     24
##  3:  7.5 -0.17968026     24
##  4: 10.5 -0.10234098     24
##  5: 13.5 -0.20286547     24
##  6: 16.5 -0.14224554     24
##  7: 19.5 -0.09181317     24
##  8: 22.5 -0.15808362     24
##  9: 25.5 -0.09377907     24
## 10: 28.5 -0.07756929     24

##     time.bin subj.diff.mean obj.diff.mean    error.diff   n    pval
##  1:      1.5             NA     0.4609367 -0.1068665217 198 0.029 *
##  2:      4.5             NA     0.4887974 -0.0105738942 302 0.68 :(
##  3:      7.5             NA     0.4757123 -0.0075705560 277 0.83 :(
##  4:     10.5             NA     0.4915204  0.0335529297 286 0.31 :(
##  5:     13.5             NA     0.4713214 -0.0006543756 306 0.96 :(
##  6:     16.5             NA     0.4868129 -0.0105873764 317 0.77 :(
##  7:     19.5             NA     0.4910932 -0.0383665766 174 0.28 :(
##  8:     22.5             NA     0.4812844  0.0030439336 271  0.9 :(
##  9:     25.5             NA     0.4878047 -0.0113157054 274 0.75 :(
## 10:     28.5             NA     0.4781364 -0.0031963553 283 0.93 :(
##     time    error.diff shapes
##  1:  1.5 -0.1068665217     24
##  2:  4.5 -0.0105738942     16
##  3:  7.5 -0.0075705560     16
##  4: 10.5  0.0335529297     16
##  5: 13.5 -0.0006543756     16
##  6: 16.5 -0.0105873764     16
##  7: 19.5 -0.0383665766     16
##  8: 22.5  0.0030439336     16
##  9: 25.5 -0.0113157054     16
## 10: 28.5 -0.0031963553     16

##     time.bin subj.diff.mean obj.diff.mean    error.diff   n      pval
##  1:      1.5             NA     0.3378392 -0.0711598943 344 0.0025 **
##  2:      4.5             NA     0.3919380 -0.0276459632 432   0.098 .
##  3:      7.5             NA     0.4163719 -0.0228318805 432   0.19 :(
##  4:     10.5             NA     0.4580837 -0.0103905222 432   0.52 :(
##  5:     13.5             NA     0.4517138 -0.0075860836 432   0.69 :(
##  6:     16.5             NA     0.4502447 -0.0114558951 432   0.51 :(
##  7:     19.5             NA     0.4442288 -0.0001713636 432   0.99 :(
##  8:     22.5             NA     0.4363635 -0.0151055495 432    0.3 :(
##  9:     25.5             NA     0.4515881  0.0176739953 432   0.22 :(
## 10:     28.5             NA     0.4315254  0.0095706668 432   0.58 :(
##     time    error.diff shapes
##  1:  1.5 -0.0711598943     24
##  2:  4.5 -0.0276459632     16
##  3:  7.5 -0.0228318805     16
##  4: 10.5 -0.0103905222     16
##  5: 13.5 -0.0075860836     16
##  6: 16.5 -0.0114558951     16
##  7: 19.5 -0.0001713636     16
##  8: 22.5 -0.0151055495     16
##  9: 25.5  0.0176739953     16
## 10: 28.5  0.0095706668     16

Per group, motor task

## Warning: Removed 300 rows containing non-finite values (stat_bin2d).

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5             NA     0.7401079 -0.23309532 46  0.001 **
##  2:      4.5             NA     0.6826685 -0.17221114 54 0.0053 **
##  3:      7.5             NA     0.6595850 -0.13510640 54   0.014 *
##  4:     10.5             NA     0.6528757 -0.15958834 54 0.0072 **
##  5:     13.5             NA     0.6614892 -0.21081771 54 3e-05 ***
##  6:     16.5             NA     0.6339280 -0.15806752 54   0.019 *
##  7:     19.5             NA     0.6203989 -0.07992182 54   0.084 .
##  8:     22.5             NA     0.6266104 -0.13166951 54   0.029 *
##  9:     25.5             NA     0.6285569 -0.14267184 54 0.0048 **
## 10:     28.5             NA     0.6137866 -0.12264488 54   0.027 *
##     time  error.diff shapes
##  1:  1.5 -0.23309532     24
##  2:  4.5 -0.17221114     24
##  3:  7.5 -0.13510640     24
##  4: 10.5 -0.15958834     24
##  5: 13.5 -0.21081771     24
##  6: 16.5 -0.15806752     24
##  7: 19.5 -0.07992182     16
##  8: 22.5 -0.13166951     24
##  9: 25.5 -0.14267184     24
## 10: 28.5 -0.12264488     24

## Warning: Removed 1410 rows containing non-finite values (stat_bin2d).

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n    pval
##  1:      1.5             NA     0.4688771 -0.068257715 156 0.35 :(
##  2:      4.5             NA     0.4911750 -0.041551932 170 0.34 :(
##  3:      7.5             NA     0.4766640 -0.046651368 156 0.25 :(
##  4:     10.5             NA     0.4818171  0.044933433 150 0.26 :(
##  5:     13.5             NA     0.4653591  0.028769131 164 0.48 :(
##  6:     16.5             NA     0.4849413  0.001858205 163 0.99 :(
##  7:     19.5             NA     0.4839334 -0.106874013 111 0.046 *
##  8:     22.5             NA     0.4613620 -0.037131958 137 0.47 :(
##  9:     25.5             NA     0.4744244 -0.041490985 145 0.35 :(
## 10:     28.5             NA     0.4676360  0.009744705 154  0.8 :(
##     time   error.diff shapes
##  1:  1.5 -0.068257715     16
##  2:  4.5 -0.041551932     16
##  3:  7.5 -0.046651368     16
##  4: 10.5  0.044933433     16
##  5: 13.5  0.028769131     16
##  6: 16.5  0.001858205     16
##  7: 19.5 -0.106874013     24
##  8: 22.5 -0.037131958     16
##  9: 25.5 -0.041490985     16
## 10: 28.5  0.009744705     16

## Warning: Removed 660 rows containing non-finite values (stat_bin2d).

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5             NA     0.3625068 0.022757467 116   0.54 :(
##  2:      4.5             NA     0.3890090 0.044145162 141   0.12 :(
##  3:      7.5             NA     0.4235370 0.003441036 141   0.92 :(
##  4:     10.5             NA     0.4219971 0.086443724 141 0.0022 **
##  5:     13.5             NA     0.4090663 0.053875029 141   0.058 .
##  6:     16.5             NA     0.4242053 0.058616310 141    0.03 *
##  7:     19.5             NA     0.4661068 0.035242400 141   0.19 :(
##  8:     22.5             NA     0.4567958 0.055267337 141   0.047 *
##  9:     25.5             NA     0.4380129 0.047888295 141   0.056 .
## 10:     28.5             NA     0.4030005 0.077680680 141 0.0029 **
##     time  error.diff shapes
##  1:  1.5 0.022757467     16
##  2:  4.5 0.044145162     16
##  3:  7.5 0.003441036     16
##  4: 10.5 0.086443724     24
##  5: 13.5 0.053875029     16
##  6: 16.5 0.058616310     24
##  7: 19.5 0.035242400     16
##  8: 22.5 0.055267337     24
##  9: 25.5 0.047888295     16
## 10: 28.5 0.077680680     24

Per group, sensory task

## Warning: Removed 810 rows containing non-finite values (stat_bin2d).
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n    pval
##  1:      1.5             NA     0.3571483 -0.06453945 83    1 :(
##  2:      4.5             NA     0.4905779 -0.47939642 84 0.25 :(
##  3:      7.5             NA     0.5073238 -0.36747499 84 0.25 :(
##  4:     10.5             NA     0.4815897 -0.02603997 84    1 :(
##  5:     13.5             NA     0.4820886 -0.31835154 84  0.5 :(
##  6:     16.5             NA     0.5032217 -0.33836142 84  0.5 :(
##  7:     19.5             NA     0.5002064 -0.29686222 84  0.5 :(
##  8:     22.5             NA     0.5266886 -0.32525332 84  0.5 :(
##  9:     25.5             NA     0.4735542 -0.13970225 84 0.75 :(
## 10:     28.5             NA     0.5185295 -0.35656381 84 0.25 :(
##     time  error.diff shapes
##  1:  1.5 -0.06453945     16
##  2:  4.5 -0.47939642     16
##  3:  7.5 -0.36747499     16
##  4: 10.5 -0.02603997     16
##  5: 13.5 -0.31835154     16
##  6: 16.5 -0.33836142     16
##  7: 19.5 -0.29686222     16
##  8: 22.5 -0.32525332     16
##  9: 25.5 -0.13970225     16
## 10: 28.5 -0.35656381     16
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 10 rows containing missing values (geom_errorbar).

## Warning: Removed 1230 rows containing non-finite values (stat_bin2d).

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n    pval
##  1:      1.5             NA     0.3985712 -0.14747456 22 0.44 :(
##  2:      4.5             NA     0.5101423 -0.06860877 81 0.24 :(
##  3:      7.5             NA     0.4807701  0.05523892 71 0.38 :(
##  4:     10.5             NA     0.5086218 -0.13870469 88 0.27 :(
##  5:     13.5             NA     0.4643639 -0.07882054 82 0.49 :(
##  6:     16.5             NA     0.4894083 -0.11720531 81 0.32 :(
##  7:     19.5             NA     0.4967375  0.03943897 39 0.62 :(
##  8:     22.5             NA     0.4963117  0.05916008 81 0.38 :(
##  9:     25.5             NA     0.5030194  0.06158308 74 0.65 :(
## 10:     28.5             NA     0.4879182 -0.01426288 75    1 :(
##     time  error.diff shapes
##  1:  1.5 -0.14747456     16
##  2:  4.5 -0.06860877     16
##  3:  7.5  0.05523892     16
##  4: 10.5 -0.13870469     16
##  5: 13.5 -0.07882054     16
##  6: 16.5 -0.11720531     16
##  7: 19.5  0.03943897     16
##  8: 22.5  0.05916008     16
##  9: 25.5  0.06158308     16
## 10: 28.5 -0.01426288     16
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## Warning: Removed 270 rows containing non-finite values (stat_bin2d).

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5             NA     0.4464811 -0.15244831  97 0.00029 ***
##  2:      4.5             NA     0.4938329 -0.08901972 132 0.00014 ***
##  3:      7.5             NA     0.4780872 -0.07169067 132   0.0088 **
##  4:     10.5             NA     0.5638033 -0.09219219 132 0.00084 ***
##  5:     13.5             NA     0.5307551 -0.08087389 132 0.00015 ***
##  6:     16.5             NA     0.5140122 -0.11152315 132 2.2e-05 ***
##  7:     19.5             NA     0.4956298 -0.05253809 132   0.0054 **
##  8:     22.5             NA     0.5211902 -0.08677411 132 0.00026 ***
##  9:     25.5             NA     0.5705292 -0.02530146 132     0.15 :(
## 10:     28.5             NA     0.5353820 -0.08634180 132   0.0016 **
##     time  error.diff shapes
##  1:  1.5 -0.15244831     24
##  2:  4.5 -0.08901972     24
##  3:  7.5 -0.07169067     24
##  4: 10.5 -0.09219219     24
##  5: 13.5 -0.08087389     24
##  6: 16.5 -0.11152315     24
##  7: 19.5 -0.05253809     24
##  8: 22.5 -0.08677411     24
##  9: 25.5 -0.02530146     16
## 10: 28.5 -0.08634180     24

Per group, logical task

## Warning: Removed 810 rows containing non-finite values (stat_bin2d).

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5             NA     0.5902832 -0.32468140 105 1.7e-06 ***
##  2:      4.5             NA     0.6261366 -0.29040113 117 1.8e-05 ***
##  3:      7.5             NA     0.6138626 -0.18468766 117      0.01 *
##  4:     10.5             NA     0.5631616 -0.07414257 117     0.23 :(
##  5:     13.5             NA     0.5572596 -0.16823972 117     0.041 *
##  6:     16.5             NA     0.5562352 -0.11099977 117     0.095 .
##  7:     19.5             NA     0.5558895 -0.09079836 117     0.036 *
##  8:     22.5             NA     0.4966207 -0.17819780 117   0.0054 **
##  9:     25.5             NA     0.4744163 -0.03838009 117     0.43 :(
## 10:     28.5             NA     0.4810727 -0.01969924 117     0.69 :(
##     time  error.diff shapes
##  1:  1.5 -0.32468140     24
##  2:  4.5 -0.29040113     24
##  3:  7.5 -0.18468766     24
##  4: 10.5 -0.07414257     16
##  5: 13.5 -0.16823972     24
##  6: 16.5 -0.11099977     16
##  7: 19.5 -0.09079836     24
##  8: 22.5 -0.17819780     24
##  9: 25.5 -0.03838009     16
## 10: 28.5 -0.01969924     16
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## Warning: Removed 750 rows containing non-finite values (stat_bin2d).

##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5             NA     0.4676032 -0.141829825 20   0.067 .
##  2:      4.5             NA     0.4469716  0.077115519 51    0.2 :(
##  3:      7.5             NA     0.4655608  0.045104317 50   0.52 :(
##  4:     10.5             NA     0.4904902  0.096365996 48 0.0098 **
##  5:     13.5             NA     0.4971273 -0.017073289 60    0.5 :(
##  6:     16.5             NA     0.4881123  0.002154773 73      1 :(
##  7:     19.5             NA     0.5150354  0.039099590 24   0.54 :(
##  8:     22.5             NA     0.5098155  0.030573864 53    0.7 :(
##  9:     25.5             NA     0.5026097  0.032340956 55   0.28 :(
## 10:     28.5             NA     0.4944961 -0.024980434 54   0.54 :(
##     time   error.diff shapes
##  1:  1.5 -0.141829825     16
##  2:  4.5  0.077115519     16
##  3:  7.5  0.045104317     16
##  4: 10.5  0.096365996     24
##  5: 13.5 -0.017073289     16
##  6: 16.5  0.002154773     16
##  7: 19.5  0.039099590     16
##  8: 22.5  0.030573864     16
##  9: 25.5  0.032340956     16
## 10: 28.5 -0.024980434     16
## Warning: Removed 3 rows containing missing values (geom_errorbar).

## Warning: Removed 750 rows containing non-finite values (stat_bin2d).

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n    pval
##  1:      1.5             NA     0.2355514 -0.074437040 131 0.16 :(
##  2:      4.5             NA     0.3099433 -0.003373171 159 0.91 :(
##  3:      7.5             NA     0.3587827  0.013769725 159  0.7 :(
##  4:     10.5             NA     0.4023180  0.001613222 159 0.96 :(
##  5:     13.5             NA     0.4239140  0.056350607 159 0.16 :(
##  6:     16.5             NA     0.4203972  0.060432004 159 0.071 .
##  7:     19.5             NA     0.3821550  0.064658014 159 0.064 .
##  8:     22.5             NA     0.3478220  0.026227101 159 0.47 :(
##  9:     25.5             NA     0.3648829  0.084304485 159 0.018 *
## 10:     28.5             NA     0.3706005  0.078014462 159 0.045 *
##     time   error.diff shapes
##  1:  1.5 -0.074437040     16
##  2:  4.5 -0.003373171     16
##  3:  7.5  0.013769725     16
##  4: 10.5  0.001613222     16
##  5: 13.5  0.056350607     16
##  6: 16.5  0.060432004     16
##  7: 19.5  0.064658014     16
##  8: 22.5  0.026227101     16
##  9: 25.5  0.084304485     24
## 10: 28.5  0.078014462     24